Abstract
America has a deaf population of an estimated 10 lakhs people. The method of communication amongst the deaf community is sign language. The American Sign Language encompasses static and dynamic signs. This paper describes the method to capture the static signs (Which are the alphabets) and then translate that signs into texts. Image processing techniques are applied on these captured images. Upon the completion of the various image processing techniques, the features are relegated by three different techniques. For training dataset convolutional neural network is used. Finally, the interpreted text output for that sign in the English Language is displayed.
Highlights
This paper proposes a method for automatically recognizing the fingerspelling in Indian Sign Language
The dataset collected of the American Sign Language has in it 26 Alphabets and 3 Special Characters which is used to write a complete group of words making sense with the avail of more preponderant, stronger and more complete system
The dataset collected of American Sign Language has 3000 images for every character with all types of possible amalgamation of lights, camera angles
Summary
Sign language is widely used by people who are unable to verbalize and auricularly discern or people who can auricularly discern but unable to verbalize. The use of these gestures is always circumscribed in the auditorily impaired - dumb community, normal people never endesvor to learn sign language. This causes an immensely colossal gap in communication between the deaf - dumb people and the mundane people. Deaf people seek the help of sign language interpreters for translating their thoughts to normal people and vice - versa. These systems are very costly and does not work throughout the life period of a deaf person. A system that automatically recognizes sign language gestures is obligatory
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